Short-term wind power forecasting in Portugal by neural networks and wavelet transform

被引：222

作者：

Catalao, J. P. S. ^{[1
,2
]}

Pousinho, H. M. I. ^{[1
]}

Mendes, V. M. F. ^{[3
]}

机构：

[1] Univ Beira Interior, Dept Electromech Engn, P-6201001 Covilha, Portugal

[2] Univ Tecn Lisboa, Inst Super Tecn, Ctr Innovat Elect & Energy Engn, P-1049001 Lisbon, Portugal

[3] Inst Super Engn Lisboa, Dept Elect Engn & Automat, P-1950062 Lisbon, Portugal

来源：

RENEWABLE ENERGY | 2011年 / 36卷 / 04期

关键词：

Wind power; Forecasting; Artificial neural networks; Wavelet transform; FEATURE-EXTRACTION; SPEED;

D O I：

10.1016/j.renene.2010.09.016

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

This paper proposes artificial neural networks in combination with wavelet transform for short-term wind power forecasting in Portugal. The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. Results from a real-world case study are presented. A comparison is carried out, taking into account the results obtained with other approaches. Finally, conclusions are duly drawn. (C) 2010 Elsevier Ltd. All rights reserved.

引用

页码：1245 / 1251

页数：7

共 30 条

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